能否通过梯度修正LSTM细胞状态？为何仅对权重计算梯度？

Great question—this cuts right to the core of how recurrent networks learn vs. how they process sequential data! Let’s break this down clearly:

The key distinction here is between the model's generalizable "skill set" (weights) and its transient, sequence-specific context (cell state):

• Cell states are temporary and task-specific
  A cell state is a dynamic, short-term store of context for the current sequence being processed. For example, when parsing "I walked to the café and ordered a ___", the cell state holds the context of "café visit". If you switched to a new sequence like "The cat climbed the tree and chased a ___", the cell state would reset and track an entirely different set of context. Modifying cell states during training only fixes the current batch's temporary context—it does nothing to help the model learn how to handle unseen sequences later.

• Weights encode the rules for managing cell states
  The weights in LSTM gates (forget, input, output) are what define how the cell state should update as new data comes in. Training is all about tuning these rules: learning when to forget old context, when to add new information, and when to use the cell state to generate outputs. Updating weights optimizes the model's core logic for all sequences, not just one.

• Training is about learning a function, not fixing a single sequence
  Machine learning aims to learn a reusable function that maps inputs to outputs. Weights are the parameters of this function. Cell states are internal variables that the function uses while running—like a calculator's temporary display. You don't improve a calculator by changing its display mid-calculation; you improve it by refining its internal circuitry (the "weights").

In theory, you could tweak cell states during training—but it would defeat the purpose of building a generalizable model:

• No generalization to new data
  Any changes you make to a cell state only apply to the current sequence or batch. When the model moves to a new batch, the cell state resets (or carries over from the previous batch, but that's still transient). Your modifications won't help the model handle unseen sequences at all.

• Severe overfitting to a single sequence
  You might force the cell state to a value that makes the current batch's output perfect, but this isn't learning—it's cheating. The model won't understand why that value works, so it can't replicate the logic for other inputs.

• Break the sequential context flow
  LSTMs rely on the gradual, gate-controlled evolution of cell states to track long-term dependencies. Forcing changes to cell states disrupts this natural flow, making it impossible for the model to properly sequence context over time.

Think of an LSTM as a bartender:

• Weights are the bartender's knowledge of recipes, customer preferences, and drink-making techniques—their core skill set that works for every customer.
• Cell state is the current tab and order details for the customer at the bar—temporary, specific to that person, and irrelevant once they leave.

Training the model is like teaching the bartender to make better drinks. You don't fix a single customer's drink to make the bartender better; you teach them better mixing skills (update weights) so they can make great drinks for everyone who comes in.

内容的提问来源于stack exchange，提问作者Kari